


'''simple docstring'''


# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= [False] * len(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			int							= [-1] * len(SCREAMING_SNAKE_CASE__	)

				def dfs(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= True
								_SCREAMING_SNAKE_CASE					:			Any							= c
								for u in graph[v]:
												if not visited[u]:
																dfs(SCREAMING_SNAKE_CASE__				, 1 - c	)

				for i in range(len(SCREAMING_SNAKE_CASE__	)	):
								if not visited[i]:
												dfs(SCREAMING_SNAKE_CASE__				, 0	)

				for i in range(len(SCREAMING_SNAKE_CASE__	)	):
								for j in graph[i]:
												if color[i] == color[j]:
																return False

				return True


# Adjacency list of graph
UpperCAmelCase_						:  Optional[Any]				  =						{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}







print(check_bipartite_dfs(graph))








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						:  int				  =						{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Tuple				  =						[
				    'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'WavLMForAudioFrameClassification',
				    'WavLMForCTC',
				    'WavLMForSequenceClassification',
				    'WavLMForXVector',
				    'WavLMModel',
				    'WavLMPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavlm import (
								    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavLMForAudioFrameClassification,
								    WavLMForCTC,
								    WavLMForSequenceClassification,
								    WavLMForXVector,
								    WavLMModel,
								    WavLMPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  Any				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




